Abstract
Recent introduction of probe-based confocal laser endomicroscopy (pCLE) allowed for the acquisition of in-vivo optical biopsies during the endoscopic examination without removing any tissue sample. The non-invasive nature of the optical biopsies makes the re-targeting of previous biopsy sites in surveillance examinations difficult due to the absence of scars or surface landmarks. In this work, we introduce a new method for recognition of optical biopsy scenes of the diagnosis endoscopy during serial surveillance examinations. To this end, together with our clinical partners, we propose a new workflow involving two-run surveillance endoscopies to reduce the ill-posedness of the task. In the first run, the endoscope is guided from the mouth to the z-line (junction from the oesophagus to the stomach). Our method relies on clustering the frames of the diagnosis and the first run surveillance (\( \mathcal{S}1 \)) endoscopy into several scenes and establishing cluster correspondences accross these videos. During the second run surveillance (\( \mathcal{S}2 \)), the scene recognition is performed in real-time and in-vivo based on the cluster correspondences. Detailed experimental results demonstrate the feasibility of the proposed approach with 89.75% recall and 80.91% precision on 3 patient datasets.
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Atasoy, S., Mateus, D., Meining, A., Yang, GZ., Navab, N. (2011). Targeted Optical Biopsies for Surveillance Endoscopies. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_11
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DOI: https://doi.org/10.1007/978-3-642-23626-6_11
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